Parameter estimation from aggregate observations: a Wasserstein distance-based sequential Monte Carlo sampler [PDF]
Royal Society Open Science, 2023 In this work, we study systems consisting of a group of moving particles. In such systems, often some important parameters are unknown and have to be estimated from observed data.
Chen Cheng, Linjie Wen, Jinglai Li
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Sequential Monte Carlo-guided ensemble tracking. [PDF]
PLoS ONE, 2017 A great deal of robustness is allowed when visual tracking is considered as a classification problem. This paper combines a finite number of weak classifiers in a SMC framework as a strong classifier.
Yuru Wang +4 more
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Sequential Monte Carlo with transformations [PDF]
Stat Comput, 2019 This paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions.
Culliford, Richard +3 more
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SeqClone: sequential Monte Carlo based inference of tumor subclones [PDF]
BMC Bioinformatics, 2019 Background Tumor samples are heterogeneous. They consist of varying cell populations or subclones and each subclone is characterized with a distinct single nucleotide variant (SNV) profile.
Oyetunji E. Ogundijo, Xiaodong Wang
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Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach [PDF]
PeerJ, 2018 Tumor samples obtained from a single cancer patient spatially or temporally often consist of varying cell populations, each harboring distinct mutations that uniquely characterize its genome.
Oyetunji E. Ogundijo, Xiaodong Wang
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A sequential Monte Carlo approach to gene expression deconvolution. [PDF]
PLoS ONE, 2017 High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of ...
Oyetunji E Ogundijo, Xiaodong Wang
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Time-Varying GPS Displacement Network Modeling by Sequential Monte Carlo [PDF]
EntropyGeodetic observations through high-rate GPS time-series data allow the precise modeling of slow ground deformation at the millimeter level. However, significant attention has been devoted to utilizing these data for various earth science applications ...
Suchanun Piriyasatit +2 more
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Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm [PDF]
Data in BriefThis article outlines the input data and partial shading conditions employed in the replication model of Sequential Monte Carlo (SMC)-based tracking techniques for photovoltaic (PV) systems.
Alhaj-Saleh A. Odat +3 more
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SMCTC: Sequential Monte Carlo in C++ [PDF]
Journal of Statistical Software, 2009 Sequential Monte Carlo methods are a very general class of Monte Carlo methodsfor sampling from sequences of distributions. Simple examples of these algorithms areused very widely in the tracking and signal processing literature.
Adam M. Johansen
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Bayesian estimation of scaled mutation rate under the coalescent: a sequential Monte Carlo approach [PDF]
BMC Bioinformatics, 2017 Background Samples of molecular sequence data of a locus obtained from random individuals in a population are often related by an unknown genealogy. More importantly, population genetics parameters, for instance, the scaled population mutation rate Θ=4N ...
Oyetunji E. Ogundijo, Xiaodong Wang
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